




"""simple docstring"""

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__magic_name__				     =     logging.get_logger(__name__)

__magic_name__				     =     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__magic_name__				     =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class        SCREAMING_SNAKE_CASE__   :
   snake_case			   =       field(
       default=snake_case__ ,				metadata={"help": "Model type selected in the list: " + ", ".join(snake_case__      )}      )
   snake_case			   =       field(
       default=snake_case__ ,				metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}      )
   snake_case			   =       field(
       default=128 ,				metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       } ,				)
   snake_case			   =       field(
       default=128 ,				metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} ,				)
   snake_case			   =       field(
       default=64 ,				metadata={
           "help": (
               "The maximum number of tokens for the question. Questions longer than this will "
               "be truncated to this length."
           )
       } ,				)
   snake_case			   =       field(
       default=30 ,				metadata={
           "help": (
               "The maximum length of an answer that can be generated. This is needed because the start "
               "and end predictions are not conditioned on one another."
           )
       } ,				)
   snake_case			   =       field(
       default=snake_case__ ,				metadata={"help": "Overwrite the cached training and evaluation sets"}      )
   snake_case			   =       field(
       default=snake_case__ ,				metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}      )
   snake_case			   =       field(
       default=0.0 ,				metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}      )
   snake_case			   =       field(
       default=20 ,				metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}      )
   snake_case			   =       field(
       default=0 ,				metadata={
           "help": (
               "language id of input for language-specific xlm models (see"
               " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
           )
       } ,				)
   snake_case			   =       field(default=1 ,				metadata={"help": "multiple threads for converting example to features"}      )






class        SCREAMING_SNAKE_CASE__   (						snake_case__      ):
   snake_case			   =       '''train'''
   snake_case			   =       '''dev'''






class        SCREAMING_SNAKE_CASE__   (						snake_case__      ):
   snake_case			   =       42
   snake_case			   =       42
   snake_case			   =       42
   snake_case			   =       42
   def __init__(       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				str   ,	SCREAMING_SNAKE_CASE_			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				List[str] = None   ,	SCREAMING_SNAKE_CASE_			:				List[str] = Split.train   ,	SCREAMING_SNAKE_CASE_			:				List[Any] = False   ,	SCREAMING_SNAKE_CASE_			:				int = None   ,	SCREAMING_SNAKE_CASE_			:				int = "pt"   ,	):
          lowerCamelCase__          =						args
          lowerCamelCase__          =						is_language_sensitive
          lowerCamelCase__          =						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
          if isinstance(_UpperCAmelCase   ,	_UpperCAmelCase							):
                 try:
                        lowerCamelCase__          =						Split[mode]
                 except KeyError:
                        raise KeyError("""mode is not a valid split name"""							)
          lowerCamelCase__          =						mode
          # Load data features from cache or dataset file
          lowerCamelCase__          =						"""v2""" if args.version_2_with_negative else """v1"""
          lowerCamelCase__          =						os.path.join(
              cache_dir if cache_dir is not None else args.data_dir   ,	f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""   ,	)

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase__          =						cached_features_file + """.lock"""
          with FileLock(_UpperCAmelCase							):
                 if os.path.exists(_UpperCAmelCase							) and not args.overwrite_cache:
                        lowerCamelCase__          =						time.time()
                        lowerCamelCase__          =						torch.load(_UpperCAmelCase							)

                        # Legacy cache files have only features, while new cache files
                        # will have dataset and examples also.
                        lowerCamelCase__          =						self.old_features["""features"""]
                        lowerCamelCase__          =						self.old_features.get("""dataset"""   ,	_UpperCAmelCase							)
                        lowerCamelCase__          =						self.old_features.get("""examples"""   ,	_UpperCAmelCase							)
                        logger.info(
                            f"""Loading features from cached file {cached_features_file} [took %.3f s]"""   ,	time.time() - start							)

                        if self.dataset is None or self.examples is None:
                               logger.warning(
                                   f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
                                   """ future run"""							)
                 else:
                        if mode == Split.dev:
                               lowerCamelCase__          =						self.processor.get_dev_examples(args.data_dir							)
                        else:
                               lowerCamelCase__          =						self.processor.get_train_examples(args.data_dir							)

                        lowerCamelCase__     ,	lowerCamelCase__          =						squad_convert_examples_to_features(
                            examples=self.examples   ,	tokenizer=_UpperCAmelCase   ,	max_seq_length=args.max_seq_length   ,	doc_stride=args.doc_stride   ,	max_query_length=args.max_query_length   ,	is_training=mode == Split.train   ,	threads=args.threads   ,	return_dataset=_UpperCAmelCase   ,	)

                        lowerCamelCase__          =						time.time()
                        torch.save(
                            {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}   ,	_UpperCAmelCase   ,	)
                        # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                        logger.info(
                            f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""							)
   def __len__(       self			:				Optional[Any]							):
          return len(self.features							)





   def __getitem__(       self			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				List[str]							):
          # Convert to Tensors and build dataset
          lowerCamelCase__          =						self.features[i]

          lowerCamelCase__          =						torch.tensor(feature.input_ids   ,	dtype=torch.long							)
          lowerCamelCase__          =						torch.tensor(feature.attention_mask   ,	dtype=torch.long							)
          lowerCamelCase__          =						torch.tensor(feature.token_type_ids   ,	dtype=torch.long							)
          lowerCamelCase__          =						torch.tensor(feature.cls_index   ,	dtype=torch.long							)
          lowerCamelCase__          =						torch.tensor(feature.p_mask   ,	dtype=torch.float							)
          lowerCamelCase__          =						torch.tensor(feature.is_impossible   ,	dtype=torch.float							)

          lowerCamelCase__          =						{
              """input_ids""": input_ids,
              """attention_mask""": attention_mask,
              """token_type_ids""": token_type_ids,
          }

          if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                 del inputs["token_type_ids"]

          if self.args.model_type in ["xlnet", "xlm"]:
                 inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}							)
                 if self.args.version_2_with_negative:
                        inputs.update({"""is_impossible""": is_impossible}							)
                 if self.is_language_sensitive:
                        inputs.update({"""langs""": (torch.ones(input_ids.shape   ,	dtype=torch.intaa							) * self.args.lang_id)}							)

          if self.mode == Split.train:
                 lowerCamelCase__          =						torch.tensor(feature.start_position   ,	dtype=torch.long							)
                 lowerCamelCase__          =						torch.tensor(feature.end_position   ,	dtype=torch.long							)
                 inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}							)

          return inputs











'''simple docstring'''

import os
import pickle
import unittest

from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
    VOCAB_FILES_NAMES,
    BertJapaneseTokenizer,
    CharacterTokenizer,
    JumanppTokenizer,
    MecabTokenizer,
    SudachiTokenizer,
    WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi

from ...test_tokenization_common import TokenizerTesterMixin

@custom_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: str									=      BertJapaneseTokenizer
			a_	: List[str]									=      False
			a_	: List[Any]									=      True






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					super().setUp()

					snake_case__	     =[
					    '[UNK]',
					    '[CLS]',
					    '[SEP]',
					    'こんにちは',
					    'こん',
					    'にちは',
					    'ばんは',
					    '##こん',
					    '##にちは',
					    '##ばんは',
					    '世界',
					    '##世界',
					    '、',
					    '##、',
					    '。',
					    '##。',
					]

					snake_case__	     =os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']						)
					with open(self.vocab_file					,   'w'					,   encoding='utf-8'						) as vocab_writer:
							vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							str:
					snake_case__	     ='こんにちは、世界。 \nこんばんは、世界。'
					snake_case__	     ='こんにちは 、 世界 。 こんばんは 、 世界 。'
					return input_text, output_text






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__     ,	snake_case__	     =self.get_input_output_texts(_UpperCAmelCase						)
					snake_case__	     =tokenizer.encode(_UpperCAmelCase					,   add_special_tokens=_UpperCAmelCase						)
					snake_case__	     =tokenizer.decode(_UpperCAmelCase					,   clean_up_tokenization_spaces=_UpperCAmelCase						)
					return text, ids






			def 						_lowercase  (     self						)							->							Dict:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							List[str]:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							str:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.tokenizer_class(self.vocab_file						)

					snake_case__	     =tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。'						)
					self.assertListEqual(_UpperCAmelCase					,   ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。']						)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [3, 12, 10, 14, 4, 9, 12, 10, 14]						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.tokenizer_class(self.vocab_file					,   word_tokenizer_type='mecab'						)
					self.assertIsNotNone(_UpperCAmelCase						)

					snake_case__	     ='こんにちは、世界。\nこんばんは、世界。'
					snake_case__	     =tokenizer.tokenize(_UpperCAmelCase						)
					self.assertListEqual(_UpperCAmelCase					,   ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。']						)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [3, 12, 10, 14, 4, 9, 12, 10, 14]						)

					snake_case__	     =os.path.join(self.tmpdirname					,   'tokenizer.bin'						)
					with open(_UpperCAmelCase					,   'wb'						) as handle:
							pickle.dump(_UpperCAmelCase					,   _UpperCAmelCase						)

					with open(_UpperCAmelCase					,   'rb'						) as handle:
							snake_case__	     =pickle.load(_UpperCAmelCase						)

					snake_case__	     =tokenizer_new.tokenize(_UpperCAmelCase						)

					self.assertListEqual(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =MecabTokenizer(mecab_dic='ipadic'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。']					,   )






			def 						_lowercase  (     self						)							->							str:
					try:
							snake_case__	     =MecabTokenizer(mecab_dic='unidic_lite'						)
					except ModuleNotFoundError:
							return

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。']					,   )






			def 						_lowercase  (     self						)							->							List[str]:
					try:
							snake_case__	     =MecabTokenizer(mecab_dic='unidic'						)
					except ModuleNotFoundError:
							return

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。']					,   )






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =MecabTokenizer(do_lower_case=_UpperCAmelCase					,   mecab_dic='ipadic'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。']					,   )






			def 						_lowercase  (     self						)							->							List[Any]:
					try:
							snake_case__	     =MecabTokenizer(
							    do_lower_case=_UpperCAmelCase					,   normalize_text=_UpperCAmelCase					,   mecab_option='-d /usr/local/lib/mecab/dic/jumandic'						)
					except RuntimeError:
							# if dict doesn't exist in the system, previous code raises this error.
							return

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。']					,   )






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =MecabTokenizer(normalize_text=_UpperCAmelCase					,   mecab_dic='ipadic'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', '　', '。']					,   )






			@require_sudachi
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.tokenizer_class(self.vocab_file					,   word_tokenizer_type='sudachi'						)
					self.assertIsNotNone(_UpperCAmelCase						)

					snake_case__	     ='こんにちは、世界。\nこんばんは、世界。'
					snake_case__	     =tokenizer.tokenize(_UpperCAmelCase						)
					self.assertListEqual(_UpperCAmelCase					,   ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。']						)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [3, 12, 10, 14, 4, 9, 12, 10, 14]						)

					snake_case__	     =os.path.join(self.tmpdirname					,   'tokenizer.bin'						)
					with open(_UpperCAmelCase					,   'wb'						) as handle:
							pickle.dump(_UpperCAmelCase					,   _UpperCAmelCase						)

					with open(_UpperCAmelCase					,   'rb'						) as handle:
							snake_case__	     =pickle.load(_UpperCAmelCase						)

					snake_case__	     =tokenizer_new.tokenize(_UpperCAmelCase						)

					self.assertListEqual(_UpperCAmelCase					,   _UpperCAmelCase						)






			@require_sudachi
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =SudachiTokenizer(sudachi_dict_type='core'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' ']					,   )






			@require_sudachi
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =SudachiTokenizer(sudachi_dict_type='core'					,   sudachi_split_mode='A'						)

					self.assertListEqual(tokenizer.tokenize('外国人参政権'						)					,   ['外国', '人', '参政', '権']						)






			@require_sudachi
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =SudachiTokenizer(sudachi_dict_type='core'					,   sudachi_split_mode='B'						)

					self.assertListEqual(tokenizer.tokenize('外国人参政権'						)					,   ['外国人', '参政権']						)






			@require_sudachi
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =SudachiTokenizer(sudachi_dict_type='core'					,   sudachi_split_mode='C'						)

					self.assertListEqual(tokenizer.tokenize('外国人参政権'						)					,   ['外国人参政権']						)






			@require_sudachi
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =SudachiTokenizer(do_lower_case=_UpperCAmelCase					,   sudachi_dict_type='core'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' ']					,   )






			@require_sudachi
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =SudachiTokenizer(normalize_text=_UpperCAmelCase					,   sudachi_dict_type='core'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' ']					,   )






			@require_sudachi
			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =SudachiTokenizer(trim_whitespace=_UpperCAmelCase					,   sudachi_dict_type='core'						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。']					,   )






			@require_jumanpp
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.tokenizer_class(self.vocab_file					,   word_tokenizer_type='jumanpp'						)
					self.assertIsNotNone(_UpperCAmelCase						)

					snake_case__	     ='こんにちは、世界。\nこんばんは、世界。'
					snake_case__	     =tokenizer.tokenize(_UpperCAmelCase						)
					self.assertListEqual(_UpperCAmelCase					,   ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。']						)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [3, 12, 10, 14, 4, 9, 12, 10, 14]						)

					snake_case__	     =os.path.join(self.tmpdirname					,   'tokenizer.bin'						)
					with open(_UpperCAmelCase					,   'wb'						) as handle:
							pickle.dump(_UpperCAmelCase					,   _UpperCAmelCase						)

					with open(_UpperCAmelCase					,   'rb'						) as handle:
							snake_case__	     =pickle.load(_UpperCAmelCase						)

					snake_case__	     =tokenizer_new.tokenize(_UpperCAmelCase						)

					self.assertListEqual(_UpperCAmelCase					,   _UpperCAmelCase						)






			@require_jumanpp
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =JumanppTokenizer()

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。']					,   )






			@require_jumanpp
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =JumanppTokenizer(do_lower_case=_UpperCAmelCase						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。']					,   )






			@require_jumanpp
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =JumanppTokenizer(normalize_text=_UpperCAmelCase						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。']					,   )






			@require_jumanpp
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =JumanppTokenizer(trim_whitespace=_UpperCAmelCase						)

					self.assertListEqual(
					    tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  '						)					,   ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。']					,   )






			@require_jumanpp
			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =JumanppTokenizer()

					self.assertListEqual(
					    tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。'						)					,   ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。']					,   )






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']

					snake_case__	     ={}
					for i, token in enumerate(_UpperCAmelCase						):
							snake_case__	     =i
					snake_case__	     =WordpieceTokenizer(vocab=_UpperCAmelCase					,   unk_token='[UNK]'						)

					self.assertListEqual(tokenizer.tokenize(''						)					,   []						)

					self.assertListEqual(tokenizer.tokenize('こんにちは'						)					,   ['こんにちは']						)

					self.assertListEqual(tokenizer.tokenize('こんばんは'						)					,   ['こん', '##ばんは']						)

					self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは'						)					,   ['こん', '##ばんは', '[UNK]', 'こんにちは']						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp'						)
					snake_case__	     =tokenizer.subword_tokenizer

					snake_case__	     =subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。']						)

					snake_case__	     =subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは']						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese'						)

					snake_case__	     =tokenizer.encode('ありがとう。'					,   add_special_tokens=_UpperCAmelCase						)
					snake_case__	     =tokenizer.encode('どういたしまして。'					,   add_special_tokens=_UpperCAmelCase						)

					snake_case__	     =tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						)
					snake_case__	     =tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase					,   _UpperCAmelCase						)

					# 2 is for "[CLS]", 3 is for "[SEP]"
					assert encoded_sentence == [2] + text + [3]
					assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: int									=      BertJapaneseTokenizer
			a_	: Optional[Any]									=      False






			def 						_lowercase  (     self						)							->							Any:
					super().setUp()

					snake_case__	     =['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']

					snake_case__	     =os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']						)
					with open(self.vocab_file					,   'w'					,   encoding='utf-8'						) as vocab_writer:
							vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)






			def 						_lowercase  (     self					,   **_UpperCAmelCase						)							->							Union[str, Any]:
					return BertJapaneseTokenizer.from_pretrained(self.tmpdirname					,   subword_tokenizer_type='character'					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     ='こんにちは、世界。 \nこんばんは、世界。'
					snake_case__	     ='こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
					return input_text, output_text






			def 						_lowercase  (     self						)							->							List[Any]:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							Tuple:
					pass  # TODO add if relevant






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.tokenizer_class(self.vocab_file					,   subword_tokenizer_type='character'						)

					snake_case__	     =tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。'						)
					self.assertListEqual(
					    _UpperCAmelCase					,   ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。']						)
					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']

					snake_case__	     ={}
					for i, token in enumerate(_UpperCAmelCase						):
							snake_case__	     =i
					snake_case__	     =CharacterTokenizer(vocab=_UpperCAmelCase					,   unk_token='[UNK]'						)

					self.assertListEqual(tokenizer.tokenize(''						)					,   []						)

					self.assertListEqual(tokenizer.tokenize('こんにちは'						)					,   ['こ', 'ん', 'に', 'ち', 'は']						)

					self.assertListEqual(tokenizer.tokenize('こんにちほ'						)					,   ['こ', 'ん', 'に', 'ち', '[UNK]']						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char'						)

					snake_case__	     =tokenizer.encode('ありがとう。'					,   add_special_tokens=_UpperCAmelCase						)
					snake_case__	     =tokenizer.encode('どういたしまして。'					,   add_special_tokens=_UpperCAmelCase						)

					snake_case__	     =tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						)
					snake_case__	     =tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase					,   _UpperCAmelCase						)

					# 2 is for "[CLS]", 3 is for "[SEP]"
					assert encoded_sentence == [2] + text + [3]
					assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     ='cl-tohoku/bert-base-japanese'
					snake_case__	     =AutoTokenizer.from_pretrained(_UpperCAmelCase						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)





class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     ='cl-tohoku/bert-base-japanese'
					with self.assertLogs('transformers'					,   level='WARNING'						) as cm:
							BertTokenizer.from_pretrained(_UpperCAmelCase						)
							self.assertTrue(
							    cm.records[0].message.startswith(
							        'The tokenizer class you load from this checkpoint is not the same type as the class this function'
							        ' is called from.'						)						)
					snake_case__	     ='bert-base-cased'
					with self.assertLogs('transformers'					,   level='WARNING'						) as cm:
							BertJapaneseTokenizer.from_pretrained(_UpperCAmelCase						)
							self.assertTrue(
							    cm.records[0].message.startswith(
							        'The tokenizer class you load from this checkpoint is not the same type as the class this function'
							        ' is called from.'						)						)




